Overview

Dataset statistics

Number of variables21
Number of observations2328
Missing cells0
Missing cells (%)0.0%
Duplicate rows252
Duplicate rows (%)10.8%
Total size in memory382.1 KiB
Average record size in memory168.1 B

Variable types

Numeric13
Categorical8

Alerts

Dataset has 252 (10.8%) duplicate rowsDuplicates
pc is highly correlated with fcHigh correlation
three_g is highly correlated with four_gHigh correlation
fc is highly correlated with pcHigh correlation
four_g is highly correlated with three_gHigh correlation
price_range is highly correlated with ramHigh correlation
ram is highly correlated with price_rangeHigh correlation
px_width is highly correlated with px_heightHigh correlation
sc_h is highly correlated with sc_wHigh correlation
sc_w is highly correlated with sc_hHigh correlation
px_height is highly correlated with px_widthHigh correlation
pc has 112 (4.8%) zeros Zeros
sc_w has 207 (8.9%) zeros Zeros
fc has 570 (24.5%) zeros Zeros

Reproduction

Analysis started2022-09-28 02:30:01.094393
Analysis finished2022-09-28 02:31:47.864297
Duration1 minute and 46.77 seconds
Software versionpandas-profiling v3.3.1
Download configurationconfig.json

Variables

talk_time
Real number (ℝ≥0)

Distinct19
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.99957045
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2022-09-27T21:31:48.214474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q16
median11
Q316
95-th percentile19
Maximum20
Range18
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.419132398
Coefficient of variation (CV)0.4926676386
Kurtosis-1.205827699
Mean10.99957045
Median Absolute Deviation (MAD)5
Skewness0.01192690442
Sum25607
Variance29.36699595
MonotonicityNot monotonic
2022-09-27T21:31:48.575420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
7150
 
6.4%
4147
 
6.3%
15140
 
6.0%
6138
 
5.9%
16135
 
5.8%
11126
 
5.4%
19125
 
5.4%
10123
 
5.3%
13123
 
5.3%
12119
 
5.1%
Other values (9)1002
43.0%
ValueCountFrequency (%)
2111
4.8%
3103
4.4%
4147
6.3%
5105
4.5%
6138
5.9%
7150
6.4%
8117
5.0%
9109
4.7%
10123
5.3%
11126
5.4%
ValueCountFrequency (%)
20115
4.9%
19125
5.4%
18118
5.1%
17110
4.7%
16135
5.8%
15140
6.0%
14114
4.9%
13123
5.3%
12119
5.1%
11126
5.4%

battery_power
Real number (ℝ≥0)

Distinct1084
Distinct (%)46.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1242.35567
Minimum501
Maximum1998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2022-09-27T21:31:49.063095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum501
5-th percentile571
Q1851.75
median1239.5
Q31614
95-th percentile1934.65
Maximum1998
Range1497
Interquartile range (IQR)762.25

Descriptive statistics

Standard deviation438.6749834
Coefficient of variation (CV)0.3530993531
Kurtosis-1.213052522
Mean1242.35567
Median Absolute Deviation (MAD)380
Skewness0.01240785486
Sum2892204
Variance192435.7411
MonotonicityNot monotonic
2022-09-27T21:31:49.514926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8329
 
0.4%
19497
 
0.3%
15897
 
0.3%
19056
 
0.3%
11546
 
0.3%
10836
 
0.3%
12816
 
0.3%
6596
 
0.3%
15696
 
0.3%
7936
 
0.3%
Other values (1074)2263
97.2%
ValueCountFrequency (%)
5012
 
0.1%
5022
 
0.1%
5033
0.1%
5045
0.2%
5062
 
0.1%
5073
0.1%
5083
0.1%
5091
 
< 0.1%
5104
0.2%
5114
0.2%
ValueCountFrequency (%)
19981
 
< 0.1%
19972
 
0.1%
19962
 
0.1%
19955
0.2%
19943
0.1%
19931
 
< 0.1%
19924
0.2%
19914
0.2%
19895
0.2%
19881
 
< 0.1%

pc
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.932560137
Minimum0
Maximum20
Zeros112
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2022-09-27T21:31:50.041308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median10
Q315
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.081752595
Coefficient of variation (CV)0.6123046335
Kurtosis-1.185576505
Mean9.932560137
Median Absolute Deviation (MAD)5
Skewness0.01870223981
Sum23123
Variance36.98771463
MonotonicityNot monotonic
2022-09-27T21:31:50.456553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
10135
 
5.8%
7134
 
5.8%
20131
 
5.6%
1131
 
5.6%
9125
 
5.4%
17120
 
5.2%
4117
 
5.0%
14115
 
4.9%
0112
 
4.8%
15112
 
4.8%
Other values (11)1096
47.1%
ValueCountFrequency (%)
0112
4.8%
1131
5.6%
2109
4.7%
3104
4.5%
4117
5.0%
575
3.2%
6111
4.8%
7134
5.8%
8110
4.7%
9125
5.4%
ValueCountFrequency (%)
20131
5.6%
1999
4.3%
1889
3.8%
17120
5.2%
16105
4.5%
15112
4.8%
14115
4.9%
13108
4.6%
1297
4.2%
1189
3.8%

three_g
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.3 KiB
1
1786 
0
542 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2328
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
11786
76.7%
0542
 
23.3%

Length

2022-09-27T21:31:50.941598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-27T21:31:51.410039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
11786
76.7%
0542
 
23.3%

Most occurring characters

ValueCountFrequency (%)
11786
76.7%
0542
 
23.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2328
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11786
76.7%
0542
 
23.3%

Most occurring scripts

ValueCountFrequency (%)
Common2328
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11786
76.7%
0542
 
23.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11786
76.7%
0542
 
23.3%

mobile_wt
Real number (ℝ≥0)

Distinct121
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.1782646
Minimum80
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2022-09-27T21:31:51.888330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile86
Q1109
median141
Q3170
95-th percentile196
Maximum200
Range120
Interquartile range (IQR)61

Descriptive statistics

Standard deviation35.41956163
Coefficient of variation (CV)0.2526751328
Kurtosis-1.220410599
Mean140.1782646
Median Absolute Deviation (MAD)31
Skewness0.01723939147
Sum326335
Variance1254.545346
MonotonicityNot monotonic
2022-09-27T21:31:52.406889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10136
 
1.5%
18233
 
1.4%
19932
 
1.4%
10531
 
1.3%
19830
 
1.3%
11429
 
1.2%
8828
 
1.2%
14628
 
1.2%
10327
 
1.2%
16026
 
1.1%
Other values (111)2028
87.1%
ValueCountFrequency (%)
8026
1.1%
8113
0.6%
8215
0.6%
8322
0.9%
8423
1.0%
8514
0.6%
8620
0.9%
8716
0.7%
8828
1.2%
8926
1.1%
ValueCountFrequency (%)
20022
0.9%
19932
1.4%
19830
1.3%
19724
1.0%
19621
0.9%
19511
 
0.5%
19417
0.7%
19317
0.7%
19220
0.9%
19118
0.8%

px_width
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1093
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1249.602234
Minimum500
Maximum1998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2022-09-27T21:31:52.922175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile574
Q1874
median1243.5
Q31621.25
95-th percentile1931.65
Maximum1998
Range1498
Interquartile range (IQR)747.25

Descriptive statistics

Standard deviation432.6186619
Coefficient of variation (CV)0.3462050965
Kurtosis-1.179500172
Mean1249.602234
Median Absolute Deviation (MAD)371.5
Skewness0.01320566552
Sum2909074
Variance187158.9066
MonotonicityNot monotonic
2022-09-27T21:31:53.528296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17679
 
0.4%
14299
 
0.4%
12478
 
0.3%
15647
 
0.3%
5197
 
0.3%
19637
 
0.3%
8747
 
0.3%
13837
 
0.3%
19236
 
0.3%
16666
 
0.3%
Other values (1083)2255
96.9%
ValueCountFrequency (%)
5004
0.2%
5012
0.1%
5031
 
< 0.1%
5061
 
< 0.1%
5074
0.2%
5081
 
< 0.1%
5092
0.1%
5103
0.1%
5112
0.1%
5122
0.1%
ValueCountFrequency (%)
19981
 
< 0.1%
19971
 
< 0.1%
19962
 
0.1%
19954
0.2%
19942
 
0.1%
19921
 
< 0.1%
19911
 
< 0.1%
19902
 
0.1%
19894
0.2%
19885
0.2%

sc_h
Real number (ℝ≥0)

HIGH CORRELATION

Distinct15
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.36340206
Minimum5
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2022-09-27T21:31:54.039416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q19
median13
Q316
95-th percentile19
Maximum19
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.236283097
Coefficient of variation (CV)0.3426470381
Kurtosis-1.194019328
Mean12.36340206
Median Absolute Deviation (MAD)4
Skewness-0.1150917925
Sum28782
Variance17.94609448
MonotonicityNot monotonic
2022-09-27T21:31:54.431141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
17223
 
9.6%
7178
 
7.6%
12177
 
7.6%
14173
 
7.4%
16167
 
7.2%
15157
 
6.7%
19153
 
6.6%
11150
 
6.4%
13148
 
6.4%
9147
 
6.3%
Other values (5)655
28.1%
ValueCountFrequency (%)
5116
5.0%
6127
5.5%
7178
7.6%
8131
5.6%
9147
6.3%
10135
5.8%
11150
6.4%
12177
7.6%
13148
6.4%
14173
7.4%
ValueCountFrequency (%)
19153
6.6%
18146
6.3%
17223
9.6%
16167
7.2%
15157
6.7%
14173
7.4%
13148
6.4%
12177
7.6%
11150
6.4%
10135
5.8%

sc_w
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct19
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.775343643
Minimum0
Maximum18
Zeros207
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2022-09-27T21:31:54.909480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile14
Maximum18
Range18
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.34391259
Coefficient of variation (CV)0.7521478996
Kurtosis-0.4029924249
Mean5.775343643
Median Absolute Deviation (MAD)3
Skewness0.6272644787
Sum13445
Variance18.86957659
MonotonicityNot monotonic
2022-09-27T21:31:55.320939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1248
10.7%
3225
9.7%
4219
9.4%
0207
8.9%
5196
8.4%
2173
 
7.4%
6157
 
6.7%
7147
 
6.3%
8143
 
6.1%
10129
 
5.5%
Other values (9)484
20.8%
ValueCountFrequency (%)
0207
8.9%
1248
10.7%
2173
7.4%
3225
9.7%
4219
9.4%
5196
8.4%
6157
6.7%
7147
6.3%
8143
6.1%
9113
4.9%
ValueCountFrequency (%)
187
 
0.3%
1723
 
1.0%
1632
 
1.4%
1540
 
1.7%
1439
 
1.7%
1355
2.4%
1283
3.6%
1192
4.0%
10129
5.5%
9113
4.9%

m_dep
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.3 KiB
0
2206 
1
 
122

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2328
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02206
94.8%
1122
 
5.2%

Length

2022-09-27T21:31:56.120346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-27T21:31:56.483061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
02206
94.8%
1122
 
5.2%

Most occurring characters

ValueCountFrequency (%)
02206
94.8%
1122
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2328
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02206
94.8%
1122
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common2328
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02206
94.8%
1122
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02206
94.8%
1122
 
5.2%

touch_screen
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.3 KiB
1
1176 
0
1152 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2328
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
11176
50.5%
01152
49.5%

Length

2022-09-27T21:31:56.807205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-27T21:31:57.169930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
11176
50.5%
01152
49.5%

Most occurring characters

ValueCountFrequency (%)
11176
50.5%
01152
49.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2328
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11176
50.5%
01152
49.5%

Most occurring scripts

ValueCountFrequency (%)
Common2328
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11176
50.5%
01152
49.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11176
50.5%
01152
49.5%

fc
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct20
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.282646048
Minimum0
Maximum19
Zeros570
Zeros (%)24.5%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2022-09-27T21:31:57.471178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile13
Maximum19
Range19
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.351548826
Coefficient of variation (CV)1.016088833
Kurtosis0.3591731713
Mean4.282646048
Median Absolute Deviation (MAD)3
Skewness1.044043135
Sum9970
Variance18.93597718
MonotonicityNot monotonic
2022-09-27T21:31:57.873032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0570
24.5%
1273
11.7%
2214
 
9.2%
3203
 
8.7%
5160
 
6.9%
4156
 
6.7%
6138
 
5.9%
7112
 
4.8%
890
 
3.9%
985
 
3.7%
Other values (10)327
14.0%
ValueCountFrequency (%)
0570
24.5%
1273
11.7%
2214
 
9.2%
3203
 
8.7%
4156
 
6.7%
5160
 
6.9%
6138
 
5.9%
7112
 
4.8%
890
 
3.9%
985
 
3.7%
ValueCountFrequency (%)
191
 
< 0.1%
1814
 
0.6%
1710
 
0.4%
1629
1.2%
1525
 
1.1%
1423
 
1.0%
1339
1.7%
1252
2.2%
1166
2.8%
1068
2.9%

four_g
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.3 KiB
1
1232 
0
1096 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2328
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
11232
52.9%
01096
47.1%

Length

2022-09-27T21:31:58.336340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-27T21:31:58.735908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
11232
52.9%
01096
47.1%

Most occurring characters

ValueCountFrequency (%)
11232
52.9%
01096
47.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2328
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11232
52.9%
01096
47.1%

Most occurring scripts

ValueCountFrequency (%)
Common2328
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11232
52.9%
01096
47.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11232
52.9%
01096
47.1%

price_range
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.3 KiB
3
593 
1
582 
2
577 
0
576 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2328
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row0
3rd row0
4th row2
5th row0

Common Values

ValueCountFrequency (%)
3593
25.5%
1582
25.0%
2577
24.8%
0576
24.7%

Length

2022-09-27T21:31:59.130350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-27T21:31:59.549327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3593
25.5%
1582
25.0%
2577
24.8%
0576
24.7%

Most occurring characters

ValueCountFrequency (%)
3593
25.5%
1582
25.0%
2577
24.8%
0576
24.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2328
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3593
25.5%
1582
25.0%
2577
24.8%
0576
24.7%

Most occurring scripts

ValueCountFrequency (%)
Common2328
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3593
25.5%
1582
25.0%
2577
24.8%
0576
24.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3593
25.5%
1582
25.0%
2577
24.8%
0576
24.7%

blue
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.3 KiB
0
1199 
1
1129 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2328
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
01199
51.5%
11129
48.5%

Length

2022-09-27T21:31:59.956430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-27T21:32:00.350548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
01199
51.5%
11129
48.5%

Most occurring characters

ValueCountFrequency (%)
01199
51.5%
11129
48.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2328
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01199
51.5%
11129
48.5%

Most occurring scripts

ValueCountFrequency (%)
Common2328
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01199
51.5%
11129
48.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01199
51.5%
11129
48.5%

n_cores
Real number (ℝ≥0)

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.522766323
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2022-09-27T21:32:00.731932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q37
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.280953195
Coefficient of variation (CV)0.5043270053
Kurtosis-1.220876308
Mean4.522766323
Median Absolute Deviation (MAD)2
Skewness-0.01285401251
Sum10529
Variance5.20274748
MonotonicityNot monotonic
2022-09-27T21:32:01.146805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4315
13.5%
7307
13.2%
5292
12.5%
3288
12.4%
8288
12.4%
1288
12.4%
6276
11.9%
2274
11.8%
ValueCountFrequency (%)
1288
12.4%
2274
11.8%
3288
12.4%
4315
13.5%
5292
12.5%
6276
11.9%
7307
13.2%
8288
12.4%
ValueCountFrequency (%)
8288
12.4%
7307
13.2%
6276
11.9%
5292
12.5%
4315
13.5%
3288
12.4%
2274
11.8%
1288
12.4%

wifi
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.3 KiB
1
1187 
0
1141 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2328
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
11187
51.0%
01141
49.0%

Length

2022-09-27T21:32:01.567558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-27T21:32:01.961027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
11187
51.0%
01141
49.0%

Most occurring characters

ValueCountFrequency (%)
11187
51.0%
01141
49.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2328
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11187
51.0%
01141
49.0%

Most occurring scripts

ValueCountFrequency (%)
Common2328
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11187
51.0%
01141
49.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11187
51.0%
01141
49.0%

dual_sim
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.3 KiB
1
1192 
0
1136 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2328
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
11192
51.2%
01136
48.8%

Length

2022-09-27T21:32:02.315760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-27T21:32:02.696623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
11192
51.2%
01136
48.8%

Most occurring characters

ValueCountFrequency (%)
11192
51.2%
01136
48.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2328
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11192
51.2%
01136
48.8%

Most occurring scripts

ValueCountFrequency (%)
Common2328
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11192
51.2%
01136
48.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11192
51.2%
01136
48.8%

ram
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1542
Distinct (%)66.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2125.13488
Minimum256
Maximum3998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2022-09-27T21:32:03.187295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum256
5-th percentile455.35
Q11205.75
median2131.5
Q33064.5
95-th percentile3833.65
Maximum3998
Range3742
Interquartile range (IQR)1858.75

Descriptive statistics

Standard deviation1084.93579
Coefficient of variation (CV)0.5105256143
Kurtosis-1.191227674
Mean2125.13488
Median Absolute Deviation (MAD)927.5
Skewness0.01959967035
Sum4947314
Variance1177085.668
MonotonicityNot monotonic
2022-09-27T21:32:03.694059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22277
 
0.3%
22436
 
0.3%
23346
 
0.3%
28446
 
0.3%
18865
 
0.2%
21075
 
0.2%
18515
 
0.2%
28225
 
0.2%
15955
 
0.2%
37045
 
0.2%
Other values (1532)2273
97.6%
ValueCountFrequency (%)
2561
 
< 0.1%
2582
0.1%
2591
 
< 0.1%
2621
 
< 0.1%
2631
 
< 0.1%
2651
 
< 0.1%
2671
 
< 0.1%
2731
 
< 0.1%
2771
 
< 0.1%
2784
0.2%
ValueCountFrequency (%)
39981
 
< 0.1%
39961
 
< 0.1%
39933
0.1%
39912
0.1%
39901
 
< 0.1%
39841
 
< 0.1%
39781
 
< 0.1%
39713
0.1%
39702
0.1%
39691
 
< 0.1%

int_memory
Real number (ℝ≥0)

Distinct63
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.30412371
Minimum2
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2022-09-27T21:32:04.496819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q116
median32
Q348
95-th percentile61
Maximum64
Range62
Interquartile range (IQR)32

Descriptive statistics

Standard deviation18.14292538
Coefficient of variation (CV)0.5616287736
Kurtosis-1.212610937
Mean32.30412371
Median Absolute Deviation (MAD)16
Skewness0.03888900439
Sum75204
Variance329.1657415
MonotonicityNot monotonic
2022-09-27T21:32:05.028517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1655
 
2.4%
2753
 
2.3%
5751
 
2.2%
749
 
2.1%
4249
 
2.1%
1448
 
2.1%
647
 
2.0%
2345
 
1.9%
3045
 
1.9%
4145
 
1.9%
Other values (53)1841
79.1%
ValueCountFrequency (%)
243
1.8%
329
1.2%
425
1.1%
539
1.7%
647
2.0%
749
2.1%
844
1.9%
942
1.8%
1043
1.8%
1135
1.5%
ValueCountFrequency (%)
6438
1.6%
6339
1.7%
6223
1.0%
6134
1.5%
6029
1.2%
5920
 
0.9%
5840
1.7%
5751
2.2%
5638
1.6%
5531
1.3%

px_height
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1125
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean645.7276632
Minimum0
Maximum1960
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2022-09-27T21:32:05.530573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile68
Q1290
median570
Q3938
95-th percentile1497.6
Maximum1960
Range1960
Interquartile range (IQR)648

Descriptive statistics

Standard deviation442.6292723
Coefficient of variation (CV)0.6854736098
Kurtosis-0.2235976414
Mean645.7276632
Median Absolute Deviation (MAD)312.5
Skewness0.6937364705
Sum1503254
Variance195920.6727
MonotonicityNot monotonic
2022-09-27T21:32:06.245057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3478
 
0.3%
1798
 
0.3%
7308
 
0.3%
3448
 
0.3%
3117
 
0.3%
5267
 
0.3%
7107
 
0.3%
6507
 
0.3%
567
 
0.3%
2867
 
0.3%
Other values (1115)2254
96.8%
ValueCountFrequency (%)
02
0.1%
11
 
< 0.1%
21
 
< 0.1%
32
0.1%
44
0.2%
51
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
82
0.1%
91
 
< 0.1%
ValueCountFrequency (%)
19601
 
< 0.1%
19491
 
< 0.1%
19201
 
< 0.1%
19143
0.1%
19011
 
< 0.1%
18991
 
< 0.1%
18951
 
< 0.1%
18781
 
< 0.1%
18741
 
< 0.1%
18692
0.1%

clock_speed
Real number (ℝ≥0)

Distinct26
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.514819588
Minimum0.5
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2022-09-27T21:32:06.762946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q10.7
median1.5
Q32.2
95-th percentile2.8
Maximum3
Range2.5
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation0.8117737835
Coefficient of variation (CV)0.5358880953
Kurtosis-1.317016443
Mean1.514819588
Median Absolute Deviation (MAD)0.8
Skewness0.1868488543
Sum3526.5
Variance0.6589766756
MonotonicityNot monotonic
2022-09-27T21:32:07.262813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.5483
20.7%
2.896
 
4.1%
2.193
 
4.0%
2.389
 
3.8%
1.686
 
3.7%
0.685
 
3.7%
2.582
 
3.5%
1.482
 
3.5%
178
 
3.4%
1.578
 
3.4%
Other values (16)1076
46.2%
ValueCountFrequency (%)
0.5483
20.7%
0.685
 
3.7%
0.769
 
3.0%
0.870
 
3.0%
0.969
 
3.0%
178
 
3.4%
1.163
 
2.7%
1.267
 
2.9%
1.377
 
3.3%
1.482
 
3.5%
ValueCountFrequency (%)
330
 
1.3%
2.968
2.9%
2.896
4.1%
2.764
2.7%
2.663
2.7%
2.582
3.5%
2.476
3.3%
2.389
3.8%
2.267
2.9%
2.193
4.0%

Interactions

2022-09-27T21:31:37.063443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:21.810864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:27.518256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:33.709683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:41.199621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:47.186399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:53.022111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:58.555185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:04.555828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:10.363169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:16.682302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:22.533764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:29.464281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:37.518062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:22.336200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:28.172412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:34.250445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:41.560380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:47.523785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:53.412162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:59.026506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:04.975527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:11.415308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:17.137631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:23.023307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:29.968240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:38.087647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:22.807692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:28.662076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:34.752745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:42.038769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:47.974566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:53.843169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:59.392209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:05.341619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:12.055212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:17.538679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:23.375844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:30.572892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:39.153027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:23.282423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:29.386318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:35.754331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:42.400252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:48.402340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:54.267054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:59.845684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:05.810916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:12.485519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:17.942803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:23.814980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:31.728371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:39.605751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:23.646801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:29.752140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:36.172552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:42.809634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:48.803150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:54.665643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:00.261751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:06.182041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:12.978678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:18.403491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:24.184414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:32.277534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:40.253845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:24.112819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:30.183441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:36.747792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:43.237916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:49.235192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:55.099733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:00.656780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:06.566848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:13.353459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:18.840232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:25.016292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:32.776332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:40.898284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:24.534474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:30.518841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:37.471152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:44.001018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:49.609530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:55.493586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:01.087093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:06.996170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:13.728751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:19.208969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:25.434780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:33.374623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:41.532010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:24.959236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:31.000718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:38.167039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:44.419616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:50.093499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:55.897663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:01.473750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:07.385250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:14.168596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:19.614340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:25.923553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:33.984838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:42.089493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:25.341275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:31.409493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:39.114158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:44.858411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:50.831019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:56.263125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:01.951595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:07.853344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:14.561958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:20.248207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:26.331541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:34.444938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:42.787245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:25.741643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:31.752482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:39.504845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:45.270688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:51.187708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:56.686762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:02.706980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:08.582546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:14.996352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:20.787441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:26.850226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:34.926985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:43.214726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:26.206233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:32.171309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:39.963186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:45.767998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:51.583115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:57.389147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:03.254635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:09.034812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:15.409201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:21.262937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:27.678439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:35.448386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:43.620450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:26.676138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:32.749513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:40.396811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:46.219065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:52.137666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:57.804157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:03.703203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:09.451119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:15.882597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:21.730133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:28.314317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:35.954426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:44.052158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:27.156132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:33.335420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:40.834398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:46.752390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:52.545099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:30:58.234534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:04.135787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:09.924254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:16.266929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:22.071068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:28.771338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T21:31:36.548002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-09-27T21:32:08.050310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-27T21:32:08.933723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-27T21:32:09.540811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-27T21:32:10.154307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-27T21:32:10.611408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-27T21:31:45.067352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-27T21:31:47.008928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

talk_timebattery_powerpcthree_gmobile_wtpx_widthsc_hsc_wm_deptouch_screenfcfour_gprice_rangebluen_coreswifidual_simramint_memorypx_heightclock_speed
01191140199169216301303040129331415631.4
1141698201160994133005100610796183632.1
2914386117616631913010100710869326832.2
319904111112101413300101213012814316571.2
413104917013510771130090015001022631992.2
51917221908816385100703161023762511791.0
64713911811378106017101810485212111.6
7718021611169631090180215011885274531.2
811106715112219891614003101801604404942.4
97102381153198511100121317103155633670.7

Last rows

talk_timebattery_powerpcthree_gmobile_wtpx_widthsc_hsc_wm_deptouch_screenfcfour_gprice_rangebluen_coreswifidual_simramint_memorypx_heightclock_speed
23181816536117617851940121304113955374470.5
2319191824141103110512201913110139582501.9
232012742140190135916110140307113557565772.2
232141731181163198851014131611389248091.4
2322141979101651630136010001701452328331.6
2323201652508912641280010214012338363131.1
232416126191861429930031102101365526050.5
232515835711011604135012000411118173231.1
232629471311971220161200012020128422211760.8
2327121218140150174610000301170116673911222.8

Duplicate rows

Most frequently occurring

talk_timebattery_powerpcthree_gmobile_wtpx_widthsc_hsc_wm_deptouch_screenfcfour_gprice_rangebluen_coreswifidual_simramint_memorypx_heightclock_speed# duplicates
126597111019291550061103102044610542.73
429471311971220161200012020128422211760.83
92193417110312991390101311112940466192.63
1036127010218918400603170131611613601.93
13315268111775118100111207002227237182.13
1531559171162155991016131611335261791.63
184605161142102615600810030039291601.03
20468510093648148002000710728294330.53
214730218912471915010100811121043022.13
22476511115142911700012081122434712770.53